Issue with simple game AI
The issue is likely in how you estimate wellness, how the error function is constructed and from what data, since you have used two known good pieces of software and probably known good derivatives for your activation functions.  The second most likely is in now the components of wellness is aggregated.  Summing squares is sometimes not representative of a solid aggregation strategy.

I'm a bit confused about the game for three reasons.


Stars enter the description without telling us whether they are not obstacles, are obsticles, or the only obstacles
At one point bitcoins collection is the objective and obstacles are the challenge and at another point bitcoins are obstacles themselves
Your inputs have distance but not direction (Is this 2-D game?)


Based on textual hints, I'm going to assume five simple things, and you can correct any misconceptions I list.


Collecting all bitcoins is part of the objective
Running into bitcoins mid-jump is considered a crash
There is some radial tolerance to landing in at a bitcoin location with some imprecision
There are other things to crash into
Your outputs are jump magnitude and direction


I see that when the bitcoins are not in the vicinity of other things, the net can be trained effectively to jump to them, but when in the vicinity of other things, the training converges on a behavior that repeatedly fails prior to completion.  I'm assuming that the failure location is not 100% repeatable because the genetic algorithm has a pseudo-random seed that changes.  Again, correct me if I misunderstand in my piecing together the scenario.

One should consider the possibility that the distance to non-bitcoin obstacles is part of the error function and the difference between the jump destination and the bitcoin is also part of the error function.  (This second one is why I prefer to call the error contour a wellness measure.)

If the bitcoin incentive does not seem to be the crash cause, in that, had the other obstacle been moved, the bitcoin would have been collected, then the first of the two wellness criteria needs a higher order contribution from the distance to the other obstacle.

There are two simple functional forms that come to mind, which could be tried that increase the alarm represented in back propagation when the probability of collision is heightened to more effectively train against collision.  Both involve determining the direct jump line to the nearest bit coin and the distance from that line to its nearest other obstacle; call that x.


$x^y$, where $y &gt; 1.0$
$e^{kx}$